蛋白质相互作用网络分析工具 - 使用ToolUniverse进行PPI网络构建、功能富集与结构数据整合

Protein Interaction Network Analysis by mims-harvard/tooluniverse

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GitHub

安装命令

npx skills add https://github.com/mims-harvard/tooluniverse --skill 'Protein Interaction Network Analysis'

数据分析科研工具生物信息学

🇨🇳中文介绍

蛋白质相互作用网络分析

使用 ToolUniverse 工具进行全面的蛋白质相互作用网络分析。通过 4 阶段工作流程分析蛋白质网络：标识符映射、网络检索、富集分析和可选的结构数据。

功能特性

✅ 标识符映射 - 将蛋白质名称转换为数据库 ID（STRING、UniProt、Ensembl） ✅ 网络检索 - 获取带有置信度分数（0-1.0）的相互作用网络 ✅ 功能富集 - GO 术语、KEGG 通路、Reactome 通路 ✅ PPI 富集 - 测试蛋白质是否形成功能模块 ✅ 结构数据 - 可选的 SAXS/SANS 溶液结构（SASBDB） ✅ 备用策略 - STRING 为主（无需 API 密钥）→ BioGRID 为辅（如果密钥可用）

使用的数据库

数据库	覆盖范围	API 密钥	用途
STRING	1400 万+ 蛋白质，5000+ 种生物	❌ 不需要	主要的相互作用来源
BioGRID	230 万+ 相互作用，80+ 种生物	✅ 需要	备用，经过人工整理的数据
SASBDB	2000+ SAXS/SANS 条目	❌ 不需要	溶液结构

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1. 单蛋白质分析

发现目标蛋白质的相互作用伙伴：

result = analyze_protein_network(
    tu=tu,
    proteins=["TP53"],  # 单个蛋白质
    species=9606,
    confidence_score=0.7
)

# 前 5 个伙伴将出现在网络中
for edge in result.network_edges[:5]:
    print(f"{edge['preferredName_A']} ↔ {edge['preferredName_B']} "
          f"(score: {edge['score']})")

2. 蛋白质复合物验证

测试蛋白质是否形成功能复合物：

# DNA 损伤响应蛋白质
proteins = ["TP53", "ATM", "CHEK2", "BRCA1", "BRCA2"]

result = analyze_protein_network(tu=tu, proteins=proteins)

# 检查 PPI 富集
if result.ppi_enrichment.get("p_value", 1.0) < 0.05:
    print("✅ Proteins form functional module!")
    print(f"   Expected edges: {result.ppi_enrichment['expected_number_of_edges']:.1f}")
    print(f"   Observed edges: {result.ppi_enrichment['number_of_edges']}")
else:
    print("⚠️  Proteins may be unrelated")

为蛋白质集合寻找富集的通路：

result = analyze_protein_network(
    tu=tu,
    proteins=["MAPK1", "MAPK3", "RAF1", "MAP2K1"],  # MAPK 通路
    confidence_score=0.7
)

# 显示前几个富集的过程
print("\nTop Enriched Pathways:")
for term in result.enriched_terms[:10]:
    print(f"  {term['term']}: p={term['p_value']:.2e}, FDR={term['fdr']:.2e}")

4. 多蛋白质网络分析

为多个蛋白质构建完整的相互作用网络：

# 细胞凋亡调节因子
proteins = ["TP53", "BCL2", "BAX", "CASP3", "CASP9"]

result = analyze_protein_network(
    tu=tu,
    proteins=proteins,
    confidence_score=0.7
)

# 导出网络供 Cytoscape 使用
import pandas as pd
df = pd.DataFrame(result.network_edges)
df.to_csv("apoptosis_network.tsv", sep="\t", index=False)

5. 使用 BioGRID 验证

使用 BioGRID 获取经过实验验证的相互作用：

# 需要在环境中设置 BIOGRID_API_KEY
result = analyze_protein_network(
    tu=tu,
    proteins=["TP53", "MDM2"],
    include_biogrid=True  # 启用 BioGRID 备用
)

print(f"Primary source: {result.primary_source}")  # "STRING" 或 "BioGRID"

6. 包含结构数据

添加 SAXS/SANS 溶液结构：

result = analyze_protein_network(
    tu=tu,
    proteins=["TP53"],
    include_structure=True  # 查询 SASBDB
)

if result.structural_data:
    print(f"\nFound {len(result.structural_data)} SAXS/SANS entries:")
    for entry in result.structural_data:
        print(f"  {entry.get('sasbdb_id')}: {entry.get('title')}")

`analyze_protein_network()` 参数

参数	类型	默认值	描述
`tu`	ToolUniverse	必需	ToolUniverse 实例
`proteins`	list[str]	必需	蛋白质标识符（基因符号、UniProt ID）
`species`	int	9606	NCBI 分类学 ID（9606=人类，10090=小鼠）
`confidence_score`	float	0.7	最小相互作用置信度（0-1）。0.4=低，0.7=高，0.9=非常高
`include_biogrid`	bool	False	如果 STRING 失败则使用 BioGRID（需要 API 密钥）
`include_structure`	bool	False	包含 SASBDB 结构数据（较慢）
`suppress_warnings`	bool	True	抑制 ToolUniverse 加载警告

物种 ID（常见）

9606 - 智人（人类）
10090 - 小家鼠（小鼠）
10116 - 褐家鼠（大鼠）
7227 - 黑腹果蝇（果蝇）
6239 - 秀丽隐杆线虫（线虫）
7955 - 斑马鱼（斑马鱼）
559292 - 酿酒酵母（酵母）

置信度分数指南

分数	级别	描述	使用场景
0.15	非常低	所有证据	探索性，假设生成
0.4	低	中等证据	默认 STRING 阈值
0.7	高	强证据	推荐 - 可靠的相互作用
0.9	非常高	最强证据	仅核心相互作用

`ProteinNetworkResult` 对象

@dataclass
class ProteinNetworkResult:
    # Phase 1: Identifier mapping
    mapped_proteins: List[Dict[str, Any]]
    mapping_success_rate: float

    # Phase 2: Network retrieval
    network_edges: List[Dict[str, Any]]
    total_interactions: int

    # Phase 3: Enrichment analysis
    enriched_terms: List[Dict[str, Any]]
    ppi_enrichment: Dict[str, Any]

    # Phase 4: Structural data (optional)
    structural_data: Optional[List[Dict[str, Any]]]

    # Metadata
    primary_source: str  # "STRING" or "BioGRID"
    warnings: List[str]

网络边格式（STRING）

{
    "stringId_A": "9606.ENSP00000269305",  # Protein A STRING ID
    "stringId_B": "9606.ENSP00000258149",  # Protein B STRING ID
    "preferredName_A": "TP53",             # Protein A name
    "preferredName_B": "MDM2",             # Protein B name
    "ncbiTaxonId": 9606,                   # Species
    "score": 0.999,                        # Combined confidence (0-1)
    "nscore": 0.0,                         # Neighborhood score
    "fscore": 0.0,                         # Gene fusion score
    "pscore": 0.0,                         # Phylogenetic profile score
    "ascore": 0.947,                       # Coexpression score
    "escore": 0.951,                       # Experimental score
    "dscore": 0.9,                         # Database score
    "tscore": 0.994                        # Text mining score
}

{
    "category": "Process",                  # GO category
    "term": "GO:0006915",                   # GO term ID
    "description": "apoptotic process",     # Term description
    "number_of_genes": 4,                   # Genes in your set
    "number_of_genes_in_background": 1234, # Genes in genome
    "p_value": 1.23e-05,                    # Enrichment p-value
    "fdr": 0.0012,                          # FDR correction
    "inputGenes": "TP53,MDM2,BAX,CASP3"    # Matching genes
}

4 阶段分析流程

┌─────────────────────────────────────────────────────────────┐
│ Phase 1: Identifier Mapping                                 │
│ ─────────────────────────────────────────────────────────── │
│ STRING_map_identifiers()                                    │
│   • Validates protein names exist in database              │
│   • Converts to STRING IDs for consistency                 │
│   • Returns mapping success rate                           │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 2: Network Retrieval                                  │
│ ─────────────────────────────────────────────────────────── │
│ PRIMARY: STRING_get_network() (no API key needed)          │
│   • Retrieves all pairwise interactions                    │
│   • Returns confidence scores by evidence type             │
│                                                             │
│ FALLBACK: BioGRID_get_interactions() (if enabled)          │
│   • Used if STRING fails or for validation                 │
│   • Requires BIOGRID_API_KEY                               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 3: Enrichment Analysis                                │
│ ─────────────────────────────────────────────────────────── │
│ STRING_functional_enrichment()                              │
│   • GO terms (Process, Component, Function)                │
│   • KEGG pathways                                           │
│   • Reactome pathways                                       │
│   • FDR-corrected p-values                                  │
│                                                             │
│ STRING_ppi_enrichment()                                     │
│   • Tests if proteins interact more than random            │
│   • Returns p-value for functional coherence               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 4: Structural Data (Optional)                         │
│ ─────────────────────────────────────────────────────────── │
│ SASBDB_search_entries()                                     │
│   • SAXS/SANS solution structures                           │
│   • Protein flexibility and conformations                   │
│   • Complements crystal/cryo-EM data                       │
└─────────────────────────────────────────────────────────────┘

# 安装 ToolUniverse（如果尚未安装）
pip install tooluniverse

# 或安装包含额外功能的版本
pip install tooluniverse[all]

可选：BioGRID API 密钥

用于 BioGRID 备用功能：

注册免费 API 密钥：https://webservice.thebiogrid.org/
添加到 .env 文件：

BIOGRID_API_KEY=your_key_here

tooluniverse-protein-interactions/
├── SKILL.md                    # This file
├── python_implementation.py    # Main implementation
├── QUICK_START.md             # Quick reference
├── DOMAIN_ANALYSIS.md         # Design rationale
└── KNOWN_ISSUES.md            # ToolUniverse limitations

1. ToolUniverse 冗长输出

问题：ToolUniverse 在分析过程中打印 40 多条警告信息。

解决方法：运行时过滤输出：

python your_script.py 2>&1 | grep -v "Error loading tools"

详情请参阅 KNOWN_ISSUES.md。

2. BioGRID 需要 API 密钥

BioGRID 备用功能需要免费的 API 密钥。STRING 无需任何 API 密钥即可工作。

3. SASBDB 可能存在 API 问题

SASBDB 端点偶尔会返回错误。结构数据是可选的。

操作	时间	备注
标识符映射	1-2 秒	针对 5 个蛋白质
网络检索	2-3 秒	取决于网络大小
富集分析	3-5 秒	针对 374 个术语
完整的 4 阶段分析	6-10 秒	不包括 ToolUniverse 开销

注意：每个工具调用需额外增加 4-8 秒的 ToolUniverse 加载时间（框架限制）。

禁用结构数据（如果不需要）：include_structure=False
使用更高的置信度分数以减少网络大小：confidence_score=0.9
过滤输出以避免处理警告信息
跨多个分析重用 ToolUniverse 实例

"错误：'protein_ids' 是必需属性"

✅ 在此技能中已修复 - 所有参数名称已在第 2 阶段测试中验证。

未找到相互作用

检查蛋白质名称是否正确（区分大小写）
尝试更低的置信度分数：confidence_score=0.4
验证物种 ID 是否正确
检查蛋白质是否确实存在相互作用（并非所有蛋白质都有已知的相互作用）

BioGRID 无法工作

确保 BIOGRID_API_KEY 已在环境中设置
在 https://webservice.thebiogrid.org/ 检查 API 密钥是否有效
BioGRID 是可选的 - STRING 无需它即可工作

这是预期的（请参阅 KNOWN_ISSUES.md）
ToolUniverse 框架在每次调用时都会重新加载工具
使用输出过滤以减少处理时间

请参阅 python_implementation.py 了解：

example_tp53_analysis() - 完整的 TP53 网络分析
analyze_protein_network() - 包含所有选项的主要函数
ProteinNetworkResult - 结果数据结构

STRING：https://string-db.org/（1400 万+ 蛋白质，5000+ 种生物）
BioGRID：https://thebiogrid.org/（230 万+ 相互作用，经过实验验证）
SASBDB：https://www.sasbdb.org/（2000+ SAXS/SANS 条目）
ToolUniverse：https://github.com/mims-harvard/ToolUniverse

对于以下问题：

此技能：请检查 KNOWN_ISSUES.md 和故障排除部分
ToolUniverse 框架：请参阅 TOOLUNIVERSE_BUG_REPORT.md
API 错误：请检查数据库状态页面（STRING、BioGRID、SASBDB）

与 ToolUniverse 框架许可证相同。

🇺🇸English

Protein Interaction Network Analysis

Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.

Features

✅ Identifier Mapping - Convert protein names to database IDs (STRING, UniProt, Ensembl) ✅ Network Retrieval - Get interaction networks with confidence scores (0-1.0) ✅ Functional Enrichment - GO terms, KEGG pathways, Reactome pathways ✅ PPI Enrichment - Test if proteins form functional modules ✅ Structural Data - Optional SAXS/SANS solution structures (SASBDB) ✅ Fallback Strategy - STRING primary (no API key) → BioGRID secondary (if key available)

Databases Used

Database	Coverage	API Key	Purpose
STRING	14M+ proteins, 5,000+ organisms	❌ Not required	Primary interaction source
BioGRID	2.3M+ interactions, 80+ organisms	✅ Required	Fallback, curated data
SASBDB	2,000+ SAXS/SANS entries	❌ Not required	Solution structures

Quick Start

Basic Usage

from tooluniverse import ToolUniverse
from python_implementation import analyze_protein_network

# Initialize ToolUniverse
tu = ToolUniverse()

# Analyze protein network
result = analyze_protein_network(
    tu=tu,
    proteins=["TP53", "MDM2", "ATM", "CHEK2"],
    species=9606,  # Human
    confidence_score=0.7  # High confidence
)

# Access results
print(f"Mapped: {len(result.mapped_proteins)} proteins")
print(f"Network: {result.total_interactions} interactions")
print(f"Enrichment: {len(result.enriched_terms)} GO terms")
print(f"PPI p-value: {result.ppi_enrichment.get('p_value', 1.0):.2e}")

Expected Output

🔍 Phase 1: Mapping 4 protein identifiers...
✅ Mapped 4/4 proteins (100.0%)

🕸️  Phase 2: Retrieving interaction network...
✅ STRING: Retrieved 6 interactions

🧬 Phase 3: Performing enrichment analysis...
✅ Found 245 enriched GO terms (FDR < 0.05)
✅ PPI enrichment significant (p=3.45e-05)

✅ Analysis complete!

Use Cases

1. Single Protein Analysis

Discover interaction partners for a protein of interest:

result = analyze_protein_network(
    tu=tu,
    proteins=["TP53"],  # Single protein
    species=9606,
    confidence_score=0.7
)

# Top 5 partners will be in the network
for edge in result.network_edges[:5]:
    print(f"{edge['preferredName_A']} ↔ {edge['preferredName_B']} "
          f"(score: {edge['score']})")

2. Protein Complex Validation

Test if proteins form a functional complex:

# DNA damage response proteins
proteins = ["TP53", "ATM", "CHEK2", "BRCA1", "BRCA2"]

result = analyze_protein_network(tu=tu, proteins=proteins)

# Check PPI enrichment
if result.ppi_enrichment.get("p_value", 1.0) < 0.05:
    print("✅ Proteins form functional module!")
    print(f"   Expected edges: {result.ppi_enrichment['expected_number_of_edges']:.1f}")
    print(f"   Observed edges: {result.ppi_enrichment['number_of_edges']}")
else:
    print("⚠️  Proteins may be unrelated")

3. Pathway Discovery

Find enriched pathways for a protein set:

result = analyze_protein_network(
    tu=tu,
    proteins=["MAPK1", "MAPK3", "RAF1", "MAP2K1"],  # MAPK pathway
    confidence_score=0.7
)

# Show top enriched processes
print("\nTop Enriched Pathways:")
for term in result.enriched_terms[:10]:
    print(f"  {term['term']}: p={term['p_value']:.2e}, FDR={term['fdr']:.2e}")

4. Multi-Protein Network Analysis

Build complete interaction network for multiple proteins:

# Apoptosis regulators
proteins = ["TP53", "BCL2", "BAX", "CASP3", "CASP9"]

result = analyze_protein_network(
    tu=tu,
    proteins=proteins,
    confidence_score=0.7
)

# Export network for Cytoscape
import pandas as pd
df = pd.DataFrame(result.network_edges)
df.to_csv("apoptosis_network.tsv", sep="\t", index=False)

5. With BioGRID Validation

Use BioGRID for experimentally validated interactions:

# Requires BIOGRID_API_KEY in environment
result = analyze_protein_network(
    tu=tu,
    proteins=["TP53", "MDM2"],
    include_biogrid=True  # Enable BioGRID fallback
)

print(f"Primary source: {result.primary_source}")  # "STRING" or "BioGRID"

6. Including Structural Data

Add SAXS/SANS solution structures:

result = analyze_protein_network(
    tu=tu,
    proteins=["TP53"],
    include_structure=True  # Query SASBDB
)

if result.structural_data:
    print(f"\nFound {len(result.structural_data)} SAXS/SANS entries:")
    for entry in result.structural_data:
        print(f"  {entry.get('sasbdb_id')}: {entry.get('title')}")

Parameters

`analyze_protein_network()` Parameters

Parameter	Type	Default	Description
`tu`	ToolUniverse	Required	ToolUniverse instance
`proteins`	list[str]	Required	Protein identifiers (gene symbols, UniProt IDs)
`species`	int	9606	NCBI taxonomy ID (9606=human, 10090=mouse)
`confidence_score`	float	0.7	Min interaction confidence (0-1). 0.4=low, 0.7=high, 0.9=very high

Species IDs (Common)

9606 - Homo sapiens (human)
10090 - Mus musculus (mouse)
10116 - Rattus norvegicus (rat)
7227 - Drosophila melanogaster (fruit fly)
6239 - Caenorhabditis elegans (worm)
7955 - Danio rerio (zebrafish)
559292 - Saccharomyces cerevisiae (yeast)

Confidence Score Guidelines

Score	Level	Description	Use Case
0.15	Very low	All evidence	Exploratory, hypothesis generation
0.4	Low	Medium evidence	Default STRING threshold
0.7	High	Strong evidence	Recommended - reliable interactions
0.9	Very high	Strongest evidence	Core interactions only

Results Structure

`ProteinNetworkResult` Object

@dataclass
class ProteinNetworkResult:
    # Phase 1: Identifier mapping
    mapped_proteins: List[Dict[str, Any]]
    mapping_success_rate: float

    # Phase 2: Network retrieval
    network_edges: List[Dict[str, Any]]
    total_interactions: int

    # Phase 3: Enrichment analysis
    enriched_terms: List[Dict[str, Any]]
    ppi_enrichment: Dict[str, Any]

    # Phase 4: Structural data (optional)
    structural_data: Optional[List[Dict[str, Any]]]

    # Metadata
    primary_source: str  # "STRING" or "BioGRID"
    warnings: List[str]

Network Edge Format (STRING)

{
    "stringId_A": "9606.ENSP00000269305",  # Protein A STRING ID
    "stringId_B": "9606.ENSP00000258149",  # Protein B STRING ID
    "preferredName_A": "TP53",             # Protein A name
    "preferredName_B": "MDM2",             # Protein B name
    "ncbiTaxonId": 9606,                   # Species
    "score": 0.999,                        # Combined confidence (0-1)
    "nscore": 0.0,                         # Neighborhood score
    "fscore": 0.0,                         # Gene fusion score
    "pscore": 0.0,                         # Phylogenetic profile score
    "ascore": 0.947,                       # Coexpression score
    "escore": 0.951,                       # Experimental score
    "dscore": 0.9,                         # Database score
    "tscore": 0.994                        # Text mining score
}

Enrichment Term Format

{
    "category": "Process",                  # GO category
    "term": "GO:0006915",                   # GO term ID
    "description": "apoptotic process",     # Term description
    "number_of_genes": 4,                   # Genes in your set
    "number_of_genes_in_background": 1234, # Genes in genome
    "p_value": 1.23e-05,                    # Enrichment p-value
    "fdr": 0.0012,                          # FDR correction
    "inputGenes": "TP53,MDM2,BAX,CASP3"    # Matching genes
}

Workflow Details

4-Phase Analysis Pipeline

┌─────────────────────────────────────────────────────────────┐
│ Phase 1: Identifier Mapping                                 │
│ ─────────────────────────────────────────────────────────── │
│ STRING_map_identifiers()                                    │
│   • Validates protein names exist in database              │
│   • Converts to STRING IDs for consistency                 │
│   • Returns mapping success rate                           │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 2: Network Retrieval                                  │
│ ─────────────────────────────────────────────────────────── │
│ PRIMARY: STRING_get_network() (no API key needed)          │
│   • Retrieves all pairwise interactions                    │
│   • Returns confidence scores by evidence type             │
│                                                             │
│ FALLBACK: BioGRID_get_interactions() (if enabled)          │
│   • Used if STRING fails or for validation                 │
│   • Requires BIOGRID_API_KEY                               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 3: Enrichment Analysis                                │
│ ─────────────────────────────────────────────────────────── │
│ STRING_functional_enrichment()                              │
│   • GO terms (Process, Component, Function)                │
│   • KEGG pathways                                           │
│   • Reactome pathways                                       │
│   • FDR-corrected p-values                                  │
│                                                             │
│ STRING_ppi_enrichment()                                     │
│   • Tests if proteins interact more than random            │
│   • Returns p-value for functional coherence               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 4: Structural Data (Optional)                         │
│ ─────────────────────────────────────────────────────────── │
│ SASBDB_search_entries()                                     │
│   • SAXS/SANS solution structures                           │
│   • Protein flexibility and conformations                   │
│   • Complements crystal/cryo-EM data                       │
└─────────────────────────────────────────────────────────────┘

Installation & Setup

Prerequisites

# Install ToolUniverse (if not already installed)
pip install tooluniverse

# Or with extras
pip install tooluniverse[all]

Optional: BioGRID API Key

For BioGRID fallback functionality:

Register for free API key: https://webservice.thebiogrid.org/
Add to .env file:

BIOGRID_API_KEY=your_key_here

Skill Files

tooluniverse-protein-interactions/
├── SKILL.md                    # This file
├── python_implementation.py    # Main implementation
├── QUICK_START.md             # Quick reference
├── DOMAIN_ANALYSIS.md         # Design rationale
└── KNOWN_ISSUES.md            # ToolUniverse limitations

Known Limitations

1. ToolUniverse Verbose Output

Issue : ToolUniverse prints 40+ warning messages during analysis.

Workaround : Filter output when running:

python your_script.py 2>&1 | grep -v "Error loading tools"

See KNOWN_ISSUES.md for details.

2. BioGRID Requires API Key

BioGRID fallback requires free API key. STRING works without any API key.

3. SASBDB May Have API Issues

SASBDB endpoints occasionally return errors. Structural data is optional.

Performance

Typical Execution Times

Operation	Time	Notes
Identifier mapping	1-2 sec	For 5 proteins
Network retrieval	2-3 sec	Depends on network size
Enrichment analysis	3-5 sec	For 374 terms
Full 4-phase analysis	6-10 sec	Excluding ToolUniverse overhead

Note : Add 4-8 seconds per tool call for ToolUniverse loading (framework limitation).

Optimization Tips

Disable structural data if not needed: include_structure=False
Use higher confidence scores to reduce network size: confidence_score=0.9
Filter output to avoid processing warning messages
Reuse ToolUniverse instance across multiple analyses

Troubleshooting

"Error: 'protein_ids' is a required property"

✅ Fixed in this skill - All parameter names verified in Phase 2 testing.

No interactions found

Check protein names are correct (case-sensitive)
Try lower confidence score: confidence_score=0.4
Verify species ID is correct
Check if proteins actually interact (not all proteins have known interactions)

BioGRID not working

Ensure BIOGRID_API_KEY is set in environment
Check API key is valid at https://webservice.thebiogrid.org/
BioGRID is optional - STRING works without it

Slow performance

This is expected (see KNOWN_ISSUES.md)
ToolUniverse framework reloads tools on every call
Use output filtering to reduce processing time

Examples

See python_implementation.py for:

example_tp53_analysis() - Complete TP53 network analysis
analyze_protein_network() - Main function with all options
ProteinNetworkResult - Result data structure

References

STRING : https://string-db.org/ (14M+ proteins, 5,000+ organisms)
BioGRID : https://thebiogrid.org/ (2.3M+ interactions, experimentally validated)
SASBDB : https://www.sasbdb.org/ (2,000+ SAXS/SANS entries)
ToolUniverse : https://github.com/mims-harvard/ToolUniverse

Support

For issues with:

This skill : Check KNOWN_ISSUES.md and troubleshooting section
ToolUniverse framework : See TOOLUNIVERSE_BUG_REPORT.md
API errors : Check database status pages (STRING, BioGRID, SASBDB)

License

Same as ToolUniverse framework license.

Weekly Installs

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Repository

mims-harvard/to…universe

GitHub Stars

1.2K

First Seen

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